---
title: "COVID-2019 in ITALIA"
author: "Pietro Boccaletto"
output:
  flexdashboard::flex_dashboard:
    orientation: rows
    #social: ["facebook", "twitter", "linkedin"]
    source_code: embed
    vertical_layout: fill
---

```{r setup, include=FALSE}
#------------------ Packages ------------------
library(flexdashboard)
# install.packages("devtools")
# devtools::install_github("RamiKrispin/coronavirus", force = TRUE)
library(coronavirus)
data(coronavirus)
update_datasets()
#View(coronavirus)
#max(coronavirus$date)

`%>%` <- magrittr::`%>%`
#------------------ Parameters ------------------
# Set colors
# https://www.w3.org/TR/css-color-3/#svg-color
confirmed_color <- "orange"
active_color <- "blue"
recovered_color <- "forestgreen"
death_color <- "red"
#------------------ Data ------------------
df <- coronavirus %>%
  dplyr::filter(date >= "2020-01-31") %>%
  dplyr::filter(Country.Region == "Italy") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(-confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

df_daily <- coronavirus %>%
  dplyr::filter(date >= "2020-01-31") %>%
  dplyr::filter(Country.Region == "Italy") %>%
  dplyr::group_by(date, type) %>%
  dplyr::summarise(total = sum(cases, na.rm = TRUE)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  dplyr::arrange(date) %>%
  dplyr::ungroup() %>%
  #dplyr::mutate(active = confirmed - death - recovered) %>%
  dplyr::mutate(active = confirmed - death) %>%
  dplyr::mutate(
    confirmed_cum = cumsum(confirmed),
    death_cum = cumsum(death),
    recovered_cum = cumsum(recovered),
    active_cum = cumsum(active)
  )


df1 <- coronavirus %>% dplyr::filter(date == max(date))
```

Sommario
=======================================================================

Row {data-width=400}
-----------------------------------------------------------------------

### confirmed {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$confirmed), big.mark = ","), "", sep = " "),
  caption = "Casi Totali Confermati",
  icon = "fas fa-user-md",
  color = confirmed_color
)
```


### death {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$death, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$death, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Casi di morte (% di mortalità)",
  icon = "fas fa-feather",
  color = death_color
)
```

### recovered {.value-box}

```{r}

valueBox(
  value = paste(format(sum(df$recovered, na.rm = TRUE), big.mark = ","), " (",
    round(100 * sum(df$recovered, na.rm = TRUE) / sum(df$confirmed), 1),
    "%)",
    sep = ""
  ),
  caption = "Guariti (% guariti)",
  icon = "fas fa-heart",
  color = recovered_color
)
```



Row
-----------------------------------------------------------------------

### **Casi cumulativi giornalieri per tipo** (Italia)

```{r}
plotly::plot_ly(data = df_daily) %>%
  plotly::add_trace(
    x = ~date,
    # y = ~active_cum,
    y = ~confirmed_cum,
    type = "scatter",
    mode = "lines+markers",
    # name = "Active",
    name = "Confermati",
    line = list(color = confirmed_color),
    marker = list(color = confirmed_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~death_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Morti",
    line = list(color = death_color),
    marker = list(color = death_color)
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~recovered_cum,
    type = "scatter",
    mode = "lines+markers",
    name = "Guariti",
    line = list(color = recovered_color),
    marker = list(color = recovered_color)
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-01-31"),
    y = 1,
    text = paste("Primo Caso"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -90
  ) %>%
  plotly::add_annotations(
    x = as.Date("2020-02-22"),
    y = 3,
    text = paste("Prima Morte"),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -90,
    ay = -90
  ) %>%
  
    plotly::add_annotations(
    x = as.Date("2020-03-9"),
    y = 14,
    text = paste(
      "Misure di contenimento",
      "
",
      "estese a tutta l'Italia"
    ),
    xref = "x",
    yref = "y",
    arrowhead = 5,
    arrowhead = 3,
    arrowsize = 1,
    showarrow = TRUE,
    ax = -10,
    ay = -110
  ) %>%
  plotly::layout(
    title = "",
    yaxis = list(title = "Numero cumulativo dei casi"),
    xaxis = list(title = "Data"),
    legend = list(x = 0.1, y = 0.9),
    hovermode = "compare"
  )
```

in Europa
=======================================================================


Column {data-width=400}
-------------------------------------


### **Nuovi casi confermati giornalmente**

```{r}
daily_confirmed <- coronavirus %>%
  dplyr::filter(type == "confirmed") %>%
  dplyr::filter(date >= "2020-02-29") %>%
  dplyr::mutate(country = Country.Region) %>%
  dplyr::group_by(date, country) %>%
  dplyr::summarise(total = sum(cases)) %>%
  dplyr::ungroup() %>%
  tidyr::pivot_wider(names_from = country, values_from = total)

#----------------------------------------
# Plotting the data

daily_confirmed %>%
  plotly::plot_ly() %>%
  plotly::add_trace(
    x = ~date,
    y = ~Italy,
    type = "scatter",
    mode = "lines+markers",
    name = "Italia"
  ) %>%
   plotly::add_trace(
     x = ~date,
     y = ~Germany,
     type = "scatter",
     mode = "lines+markers",
     name = "Germania"
   ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~Spain,
    type = "scatter",
    mode = "lines+markers",
    name = "Spain"
  ) %>%
  plotly::add_trace(
    x = ~date,
    y = ~France,
    type = "scatter",
    mode = "lines+markers",
    name = "Francia"
  ) %>%
  plotly::layout(
    title = "",
    legend = list(x = 0.1, y = 0.9),
    yaxis = list(title = "Numbero di nuovi casi confermati"),
    xaxis = list(title = "Data"),
    # paper_bgcolor = "black",
    # plot_bgcolor = "black",
    # font = list(color = 'white'),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```

### **Distribuzione dei casi per tipo**

```{r daily_summary}
df_EU <- coronavirus %>%
  # dplyr::filter(date == max(date)) %>%
  dplyr::filter(Country.Region == "Italy" |
    Country.Region == "France" | 
    Country.Region == "Germany"|
    Country.Region == "Spain") %>%
  dplyr::group_by(Country.Region, type) %>%
  dplyr::summarise(total = sum(cases)) %>%
  tidyr::pivot_wider(
    names_from = type,
    values_from = total
  ) %>%
  # dplyr::mutate(unrecovered = confirmed - ifelse(is.na(recovered), 0, recovered) - ifelse(is.na(death), 0, death)) %>%
  dplyr::mutate(unrecovered = confirmed - ifelse(is.na(death), 0, death)) %>%
  dplyr::arrange(confirmed) %>%
  dplyr::ungroup() %>%
  dplyr::mutate(country = dplyr::if_else(Country.Region == "United Arab Emirates", "UAE", Country.Region)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "Mainland China", "China", country)) %>%
  dplyr::mutate(country = dplyr::if_else(country == "North Macedonia", "N.Macedonia", country)) %>%
  dplyr::mutate(country = trimws(country)) %>%
  dplyr::mutate(country = factor(country, levels = country))

plotly::plot_ly(
  data = df_EU,
  x = ~country,
  # y = ~unrecovered,
  y = ~ confirmed,
  # text =  ~ confirmed,
  # textposition = 'auto',
  type = "bar",
  name = "Confermati",
  marker = list(color = confirmed_color)
) %>%
  plotly::add_trace(
    y = ~death,
    # text =  ~ death,
    # textposition = 'auto',
    name = "Morti",
    marker = list(color = death_color)
  ) %>%
  plotly::layout(
    barmode = "stack",
    yaxis = list(title = "Casi totali"),
    xaxis = list(title = ""),
    hovermode = "compare",
    margin = list(
      # l = 60,
      # r = 40,
      b = 10,
      t = 10,
      pad = 2
    )
  )
```


Mappa dei contagi
=======================================================================

### **Mappa mondiale dei contagi** (*Utilizza le icone + e - per ingrandire o rimpicciolire *)

```{r}
# map tab added by Art Steinmetz
library(leaflet)
library(leafpop)
library(purrr)
cv_data_for_plot <- coronavirus %>%
  # dplyr::filter(Country.Region == "Belgium") %>%
  dplyr::filter(cases > 0) %>%
  dplyr::group_by(Country.Region, Province.State, Lat, Long, type) %>%
  dplyr::summarise(cases = sum(cases)) %>%
  dplyr::mutate(log_cases = 2 * log(cases)) %>%
  dplyr::ungroup()
cv_data_for_plot.split <- cv_data_for_plot %>% split(cv_data_for_plot$type)
pal <- colorFactor(c("orange", "red", "green"), domain = c("confirmed", "death", "recovered"))
map_object <- leaflet() %>% addProviderTiles(providers$Stamen.Toner)
names(cv_data_for_plot.split) %>%
  purrr::walk(function(df) {
    map_object <<- map_object %>%
      addCircleMarkers(
        data = cv_data_for_plot.split[[df]],
        lng = ~Long, lat = ~Lat,
        #                 label=~as.character(cases),
        color = ~ pal(type),
        stroke = FALSE,
        fillOpacity = 0.8,
        radius = ~log_cases,
        popup = leafpop::popupTable(cv_data_for_plot.split[[df]],
          feature.id = FALSE,
          row.numbers = FALSE,
          zcol = c("type", "cases", "Country.Region", "Province.State")
        ),
        group = df,
        #                 clusterOptions = markerClusterOptions(removeOutsideVisibleBounds = F),
        labelOptions = labelOptions(
          noHide = F,
          direction = "auto"
        )
      )
  })

map_object %>%
  addLayersControl(
    overlayGroups = names(cv_data_for_plot.split),
    options = layersControlOptions(collapsed = FALSE)
  )
```





A riguardo
=======================================================================

```{r}
sprintf("%s - Grafici e dati aggiornati e ricalcolati automaticamente.", date())
```


**Coronavirus Dashboard: il caso in Italia**

Questa Dashboard vuole essere un aiuto alla popolazione Italiana di tenersi aggiornata a riguardo la pandemia mondiale data da COVID-19, causata dal virus SARS-CoV-2 ( 2019 severe acute respiratory syndrome coronavirus 2 ).
Con focus sulla situazione Italiana, in confronto ai paesi Europei che ci stanno vicino, come Germania, Spagna e Francia.  


La Dashboard è un estratto del lavoro di Rami Krispin consultabile qui:
[dashboard](https://ramikrispin.github.io/coronavirus_dashboard/){target="_blank"}
e delle sue modifiche apportate da Antoine Soetewey per il Belgio:
[Belgium Dashboard](https://github.com/AntoineSoetewey/coronavirus_dashboard){target="_blank"}

Io ho implementato alcune modifiche necessarie per personalizzare e calcolare i dati per il nostro paese l'Italia. 




**Codice**

Questo sito è stato sviluppato in R, un linguaggio di programmazione orientato alla statistica e il framework R Markdown. Il codice di questa Dashboard è disponibile su GitHub [GitHub](https://github.com/akaped/coronavirus_dashboard_italy){target="_blank"} ed è liberamente modificabile.


**Dati**

I dati che vengono utilizzati da questa dashboard fanno parte del pacchetto R sul coronavirus e vengono aggiornati quotidianamente, come anche la dashboard. I dati grezzi sono prelevati dal Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus [repository](https://github.com/RamiKrispin/coronavirus-csv).


**Sul Autore**

Mi chiamo Pietro Boccaletto, sono un Biologo che si occupa di Bioinformatica nel campo degli RNA non codificanti. Sviluppo e mantengo tools e database che sono d'aiuto al avanzamento della ricerca scientifica.




**Contatti**

Per ogni domanda o Feedback, potete contattarmi a questo indirizzo email: